一种融合地理位置信息的协同过滤推荐算法

目前,基于用户消费数据构建的推荐系统在电子商务领域发挥着越来越大的作用,而在这些数据中,商家本身具有的地理位置信息忠实地记录了用户的消费痕迹,能够有效反映出用户在地理位置维度上的个人偏好信息,从而对推荐系统具有非常重要的意义。现有工作一般只利用了用户对地点的评价以及地点之间的距离,无法反映出不同地点之间的关联关系,以及用户在不同地点中的偏好权重问题。该文从地理区域划分的角度出发,研究了用户在区域范围内的消费兴趣偏好,以及不同粒度级别的区域划分方法对推荐模型的影响,探索了在推荐过程中有效融合地域信息的方法,考虑了包括地区的全局性影响、用户对地区的偏好等,结合这些因素提出了融合地理位置信息的推荐模型LGE、LGN及LRSVD。通过在Yelp数据集上的实验表明,这些模型相比于传统的推荐算法能够有效提高预测效果。

A Collaborative Filtering Algorithm Combing Location Information

Recommendation system based on users consumption data is playing an increasingly large application value in e-commerce, And in these data, businesses location information which can effectively reflect the users personal geographical preference, would make an important significance on recommender system. Existing work generally use only users review data as well as the distance between locations, which cannot reflect the relationships between different locations, not to mention that user preferences in different locations should has different weight. This paper proceed from the perspective of geographical area, and study the users preferences within the area, as well as the impact of different area partition methods on recommend models. Then we explore to incorporate recommender systems with geographical information effectively, including the locations global effects and users regional preferences, proposing recommendation models, such as LGE, LGN and LRSVD. Experimental evaluation on Yelp dataset demonstrates that our models can effectively improve the prediction results comparing to the traditional methods.